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Merge pull request #10513 from pengli:dnn

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This commit is contained in:
Alexander Alekhin 2018-01-09 19:24:28 +00:00
commit 4d4f291553
7 changed files with 246 additions and 17 deletions
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65
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -22,6 +22,7 @@ class BatchNormLayerImpl : public BatchNormLayer
{
public:
Mat weights_, bias_;
Mat weightMat, biasMat;
BatchNormLayerImpl(const LayerParams& params)
{
@ -96,17 +97,81 @@ public:
return true;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
{
if (inputs[0]->dims == 4)
{
int groups = inputs[0]->size[0];
int channels = inputs[0]->size[1];
int rows = inputs[0]->size[2];
int cols = inputs[0]->size[3];
MatShape s = shape(groups * channels, rows * cols);
weightMat = Mat(s[0], s[1], CV_32FC1);
biasMat = Mat(s[0], s[1], CV_32FC1);
for (int n = 0; n < s[0]; n++)
{
weightMat.row(n).setTo(weights_.at<float>(n % channels));
biasMat.row(n).setTo(bias_.at<float>(n % channels));
}
}
}
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide();
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
CV_Assert(blobs.size() >= 2);
CV_Assert(inputs.size() == 1);
UMat &inpBlob = inputs[0];
CV_Assert(inpBlob.dims == 2 || inpBlob.dims == 4);
int groups = inpBlob.size[0];
int channels = inpBlob.size[1];
int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
for (size_t ii = 0; ii < outputs.size(); ii++)
{
if (inpBlob.dims == 2)
{
UMat& src = inputs[ii];
UMat& dst = outputs[ii];
multiply(src, weights_, dst);
add(dst, bias_, dst);
}
else
{
MatShape s = shape(groups * channels, rows * cols);
UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
multiply(src, weightMat, dst);
add(dst, biasMat, dst);
}
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}

16
modules/dnn/src/layers/blank_layer.cpp
View File

@ -63,8 +63,22 @@ public:
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
for (int i = 0, n = outputs.size(); i < n; ++i)
{
void *src_handle = inputs[i].handle(ACCESS_READ);
void *dst_handle = outputs[i].handle(ACCESS_WRITE);
if (src_handle != dst_handle)
inputs[i].copyTo(outputs[i]);
}
return true;
}
#endif

52
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -259,11 +259,63 @@ public:
}
};
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
switch (op)
{
case SUM:
if (coeffs.empty())
{
add(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
add(outputs[0], inputs[i], outputs[0]);
}
else
{
UMat mul0, mul1;
multiply(coeffs[0], inputs[0], mul0);
multiply(coeffs[1], inputs[1], mul1);
add(mul0, mul1, outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
{
multiply(coeffs[i], inputs[i], mul0);
add(mul0, outputs[0], outputs[0]);
}
}
break;
case PROD:
multiply(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
multiply(inputs[i], outputs[0], outputs[0]);
break;
case MAX:
max(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
max(inputs[i], outputs[0], outputs[0]);
break;
default:
return false;
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}

63
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -69,11 +69,74 @@ public:
return true;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
std::vector<UMat> internals;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
CV_Assert(inputs.size() == 1 && outputs.size() == 1);
CV_Assert(inputs[0].total() == outputs[0].total());
const UMat& inp0 = inputs[0];
UMat& buffer = internals[0];
size_t num = inp0.size[0];
size_t channels = inp0.size[1];
size_t channelSize = inp0.total() / (num * channels);
for (size_t i = 0; i < num; ++i)
{
MatShape s = shape(channels, channelSize);
UMat src = inputs[i].reshape(1, s.size(), &s[0]);
UMat dst = outputs[i].reshape(1, s.size(), &s[0]);
UMat abs_mat;
absdiff(src, cv::Scalar::all(0), abs_mat);
pow(abs_mat, pnorm, buffer);
if (acrossSpatial)
{
// add eps to avoid overflow
float absSum = sum(buffer)[0] + epsilon;
float norm = pow(absSum, 1.0f / pnorm);
multiply(src, 1.0f / norm, dst);
}
if (!blobs.empty())
{
// scale the output
Mat scale = blobs[0];
if (scale.total() == 1)
{
// _scale: 1 x 1
multiply(dst, scale.at<float>(0, 0), dst);
}
else
{
// _scale: _channels x 1
CV_Assert(scale.total() == channels);
repeat(scale, 1, dst.cols, buffer);
multiply(dst, buffer, dst);
}
}
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}

5
modules/dnn/test/test_layers.cpp
View File

@ -320,6 +320,11 @@ TEST(Layer_Test_Eltwise, Accuracy)
testLayerUsingCaffeModels("layer_eltwise");
}
OCL_TEST(Layer_Test_Eltwise, Accuracy)
{
testLayerUsingCaffeModels("layer_eltwise", DNN_TARGET_OPENCL);
}
TEST(Layer_Test_PReLU, Accuracy)
{
testLayerUsingCaffeModels("layer_prelu", DNN_TARGET_CPU, true);

47
modules/dnn/test/test_tf_importer.cpp
View File

@ -76,7 +76,7 @@ static std::string path(const std::string& file)
return findDataFile("dnn/tensorflow/" + file, false);
}
static void runTensorFlowNet(const std::string& prefix, bool hasText = false,
static void runTensorFlowNet(const std::string& prefix, int targetId = DNN_TARGET_CPU, bool hasText = false,
double l1 = 1e-5, double lInf = 1e-4,
bool memoryLoad = false)
{
@ -104,6 +104,9 @@ static void runTensorFlowNet(const std::string& prefix, bool hasText = false,
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(targetId);
cv::Mat input = blobFromNPY(inpPath);
cv::Mat target = blobFromNPY(outPath);
@ -132,6 +135,11 @@ TEST(Test_TensorFlow, eltwise_add_mul)
runTensorFlowNet("eltwise_add_mul");
}
OCL_TEST(Test_TensorFlow, eltwise_add_mul)
{
runTensorFlowNet("eltwise_add_mul", DNN_TARGET_OPENCL);
}
TEST(Test_TensorFlow, pad_and_concat)
{
runTensorFlowNet("pad_and_concat");
@ -141,7 +149,14 @@ TEST(Test_TensorFlow, batch_norm)
{
runTensorFlowNet("batch_norm");
runTensorFlowNet("fused_batch_norm");
runTensorFlowNet("batch_norm_text", true);
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true);
}
OCL_TEST(Test_TensorFlow, batch_norm)
{
runTensorFlowNet("batch_norm", DNN_TARGET_OPENCL);
runTensorFlowNet("fused_batch_norm", DNN_TARGET_OPENCL);
runTensorFlowNet("batch_norm_text", DNN_TARGET_OPENCL, true);
}
TEST(Test_TensorFlow, pooling)
@ -179,15 +194,15 @@ TEST(Test_TensorFlow, fp16)
{
const float l1 = 1e-3;
const float lInf = 1e-2;
runTensorFlowNet("fp16_single_conv", false, l1, lInf);
runTensorFlowNet("fp16_deconvolution", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf);
runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_even", false, l1, lInf);
runTensorFlowNet("fp16_padding_same", false, l1, lInf);
runTensorFlowNet("fp16_single_conv", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_deconvolution", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_padding_valid", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_pad_and_concat", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_even", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_padding_same", DNN_TARGET_CPU, false, l1, lInf);
}
TEST(Test_TensorFlow, quantized)
@ -267,7 +282,7 @@ OCL_TEST(Test_TensorFlow, MobileNet_SSD)
TEST(Test_TensorFlow, lstm)
{
runTensorFlowNet("lstm", true);
runTensorFlowNet("lstm", DNN_TARGET_CPU, true);
}
TEST(Test_TensorFlow, split)
@ -284,11 +299,11 @@ TEST(Test_TensorFlow, memory_read)
{
double l1 = 1e-5;
double lInf = 1e-4;
runTensorFlowNet("lstm", true, l1, lInf, true);
runTensorFlowNet("lstm", DNN_TARGET_CPU, true, l1, lInf, true);
runTensorFlowNet("batch_norm", false, l1, lInf, true);
runTensorFlowNet("fused_batch_norm", false, l1, lInf, true);
runTensorFlowNet("batch_norm_text", true, l1, lInf, true);
runTensorFlowNet("batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
runTensorFlowNet("fused_batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
}
}

15
modules/dnn/test/test_torch_importer.cpp
View File

@ -170,6 +170,11 @@ TEST(Torch_Importer, run_batch_norm)
runTorchNet("net_batch_norm", DNN_TARGET_CPU, "", false, true);
}
OCL_TEST(Torch_Importer, run_batch_norm)
{
runTorchNet("net_batch_norm", DNN_TARGET_OPENCL, "", false, true);
}
TEST(Torch_Importer, net_prelu)
{
runTorchNet("net_prelu");
@ -225,6 +230,11 @@ TEST(Torch_Importer, net_normalize)
runTorchNet("net_normalize", DNN_TARGET_CPU, "", false, true);
}
OCL_TEST(Torch_Importer, net_normalize)
{
runTorchNet("net_normalize", DNN_TARGET_OPENCL, "", false, true);
}
TEST(Torch_Importer, net_padding)
{
runTorchNet("net_padding", DNN_TARGET_CPU, "", false, true);
@ -237,6 +247,11 @@ TEST(Torch_Importer, net_non_spatial)
runTorchNet("net_non_spatial", DNN_TARGET_CPU, "", false, true);
}
OCL_TEST(Torch_Importer, net_non_spatial)
{
runTorchNet("net_non_spatial", DNN_TARGET_OPENCL, "", false, true);
}
TEST(Torch_Importer, ENet_accuracy)
{
Net net;